Long-term prediction of mortality by heart rate turbulence in hemodialysis patients and the impact of diabetes mellitus–a longitudinal observational study
摘要
Diabetes-driven impaired autonomic nervous system function might contribute to increased mortality in hemodialysis patients. Our study aimed to validate heart rate turbulence as a long-term predictor of mortality in this vulnerable cohort.
MethodsHeart rate turbulence is a non-invasive, 24 h electrocardiography-Holter-based assessment of cardiovascular autonomic responses. Hemodialysis patients of the “rISk strAtification in end-stage Renal disease” (ISAR) study, a prospective, multicenter observational study, were followed up for six years. Mortality hazard, and correlations between clinical characteristics and mortality, were assessed using Cox regression models.
ResultsHeart rate turbulence measurement at baseline was available in 290 hemodialysis patients, 99 (34%) with diabetes mellitus. In a multivariable analysis, abnormal heart rate turbulence was associated with a 2.1-fold (95% CI: 1.4–3.2; p < 0.001) increased risk for all-cause and 3.1-fold (95% CI: 1.5–6.2; p = 0.001) increased risk for cardiovascular mortality. The co-occurrence of abnormal heart rate turbulence and diabetes mellitus represented the strongest risk constellation, increasing all-cause mortality risk to a hazard ratio of 5.8 (95% CI: 3.3—10.4; p < 0.001) and cardiovascular mortality risk to 6.1 (95% CI: 2.5—15.1; p < 0.001). This association with mortality risk remained significant after multivariate adjustment. The interaction term between the two comorbidities indicated an approximately additive effect on mortality risk.
ConclusionsHeart rate turbulence significantly contributed to the prediction of long-term mortality risk in hemodialysis patients. Diabetes mellitus is a major driver of cardiovascular autonomic dysfunction, which plays a crucial role in mortality among dialysis patients. Heart rate turbulence measurement identifies high-risk patients in the dialysis setting, enhancing precision in risk prediction and stratification, and allowing an opportunity for personalized monitoring and prevention.
Graphical abstract